Mental Health Concerns in the Pediatric Population Following COVID-19

The COVID-19 pandemic has contributed to an increase in stress and mental health concerns in the pediatric population. Research indicates a decrease in pediatric mental health incidences in 2020, followed by an increase in 2021 and 2022. It is thought that the initial decrease may be due to the intensity of the pandemic at the time and a lack of hospital beds available. The increase suggests a response to the high levels of adversity and stress children and adolescents faced both during and after the origination of the pandemic. The evidence supports the idea that the COVID-19 pandemic had a negative impact on pediatric mental health. In the pediatric population, one must understand how educational resources and supportive activities for resilience and emotional coping could affect children and adolescents who were impacted by the COVID-19 pandemic. A literature review was conducted on the effects of COVID-19 on child and adolescent mental health. A toolkit for grades kindergarten to fifth was developed which includes educational support and descriptions of activities. The toolkit is intended to teach the pediatric population about mental health as well as how to cope with emotions in a healthy way. The toolkit will be distributed to third semester nursing students for use in their pediatric community health clinical experiences. Dissemination of this project will involve sharing the literature review and components of the toolkit with the Kirkhof College of Nursing Coordinated Approach to Child Health (CATCH) clinical instructors. Plans have been made to implement the project into the NUR 367 pediatric community health course during the Winter 2023 semester. The toolkit will be used in the pediatric nursing community health clinical setting at Grand Valley State University

Label Dependent Evolutionary Feature Weighting for Remote Sensing Data

Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and land cover (LULC) maps. Evolutive computation has often been used to obtain feature weighting in order to improve the results of the NN. In this paper, a new algorithm based on evolutionary computation which has been called Label Dependent Feature Weighting (LDFW) is proposed. The LDFW method transforms the feature space assigning different weights to every feature depending on each class. This multilevel feature weighting algorithm is tested on remote sensing data from fusion of sensors (LIDAR and orthophotography). The results show an improvement on the NN and resemble the results obtained with a neural network which is the best classifier for the study area

Co-evolution of Structures and Controllers for Neubot Underwater Modular Robots

This article presents the first results of a project in underwater modular robotics, called Neubots. The goals of the projects are to explore, following Von Neumann’s ideas, potential mechanisms underlying self-organization and self-replication. We briefly explain the design features of the module units. We then present simulation results of the artificial co-evolution of body structures and neural controllers for locomotion. The neural controllers are inspired from the central pattern generators underlying locomotion in vertebrate animals. They are composed of multiple neural oscillators which are connected together by a specific type of coupling called synaptic spreading. The co-evolution of body and controller leads to interesting robots capable of efficient swimming. Interesting features of the neural controllers include the possibility to modulate the speed of locomotion by varying simple input signals, the robustness against perturbations, and the distributed nature of the controllers which makes them well suited for modular robotics

Evolutionary design of soft-bodied animats with decentralized control

We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat’s morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment